{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This example irradiates 1 kg of water for 100 days in ORIGEN 2.2, increasing the  capture cross section of Hydrogen-1 by 10% each time.  The Hydrogen-2 concentration  is then gathered and displayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from subprocess import check_call\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "matplotlib.rc('font', family='serif', size=14)\n",
    "import matplotlib.pyplot as plt\n",
    "from pyne import origen22\n",
    "from pyne.material import Material\n",
    "\n",
    "NLBS = (219, 220, 221)\n",
    "\n",
    "# 1 kg of water\n",
    "water = Material()\n",
    "water.from_atom_frac({'H1': 2.0, 'O16': 1.0})\n",
    "water.mass = 1E3\n",
    "\n",
    "# Make a tape4 file for water\n",
    "origen22.write_tape4(water)\n",
    "\n",
    "# Make a tape 5 for this calculation\n",
    "#   * Just output the concentration tables\n",
    "#   * The cross-section library numbers must \n",
    "#     the library / deck numbers in tape9 \n",
    "origen22.write_tape5_irradiation(\"IRF\", 1000.0, 4E14, \n",
    "                                 xsfpy_nlb=NLBS, \n",
    "                                 out_table_num=[5])\n",
    "\n",
    "# Grab a base tape9 from which we will overlay new values\n",
    "# This must be supplied by the user\n",
    "base_tape9 = origen22.parse_tape9(\"BASE_TAPE9.INP\")\n",
    "base_h1_xs = base_tape9[NLBS[0]]['sigma_gamma'][10010]\n",
    "\n",
    "# Init a dumb overlay tape9\n",
    "overlay_tape9 = {NLBS[0]: {'_type': 'xsfpy', \n",
    "                           '_subtype': 'activation_products', \n",
    "                           'sigma_gamma': {10010: base_h1_xs}, \n",
    "                           }\n",
    "                }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2b31dd0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y2Fi8+uqr+PPPP9GoUSMAwLp167B582bk5eVV2QefNEVElmrp0qU4ePAgkpOT9T/PSHxS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\nh8PhcDg64P8AMQJEakGX9OwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x33a0190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run origen, increasing the cross section each time.\n",
    "NPOINTS = 10\n",
    "h2_concentration = []\n",
    "for i in range(NPOINTS + 1):\n",
    "    overlay_tape9[NLBS[0]]['sigma_gamma'][10010] = (1.0 + i*0.1) * base_h1_xs\n",
    "\n",
    "    # Merge the base and overlay, and write out\n",
    "    new_tape9 = origen22.merge_tape9([overlay_tape9, base_tape9])\n",
    "    origen22.write_tape9(new_tape9, 'TAPE9.INP')\n",
    "\n",
    "    # Run and parse origen output\n",
    "    rtn = check_call(['o2_therm_linux.exe'])\n",
    "    tape6 = origen22.parse_tape6('TAPE6.OUT')\n",
    "    h2_concentration.append(tape6['table_5']['summary']['activation_products']['H2'][-1])\n",
    "\n",
    "# plot results\n",
    "fig = plt.figure(figsize=(7, 7))\n",
    "plt.plot((1.0 + np.arange(NPOINTS + 1)*0.1)*base_h1_xs, h2_concentration, 'ko-', label='H2', figure=fig)\n",
    "plt.xlabel('H1 capture cross section [barns]')\n",
    "plt.ylabel('H2 concentration [grams]')\n",
    "plt.legend(loc=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean up\n",
    "for f in os.listdir('.'):\n",
    "    if (f.endswith('.INP') or f.endswith('.OUT')) and f != 'BASE_TAPE9.INP':\n",
    "        os.remove(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
